My thesis focuses on recognition and interventions of users’ activities using open-ended and flexible software. In such exploratory settings, users’ behavior is characterized by exploration, mistakes and trial-and-error. Exploratory domains provide a flexible and rich interaction environment for their users, but induce challenges for automatic recognition and support of their activities. My thesis focuses on the following three challenges which are central to understanding users’ interactions in exploratory settings and to use this understanding in order to provide them with effective support and guidance: (1) Representing and inferring users’ interactions in exploratory domains. (2) Disambiguating between possible explanations in order to improve understanding of users’ behavior. (3) Producing machine-generated support that adapts to the needs of the users. My research activities combines computational models, algorithms and empirical methodologies to meet the challenges above. They are conducted in the context of various types of exploratory settings. Specifically, I am developing novel plan recognition algorithms for inferring users’ interactions in exploratory settings and intervention mechanisms for these environments. I am evaluating my approach in the real world using educational software, medical records and cyber security domains. My results so far include (1) design of a new model for plan recognition; (2) an online plan recognition algorithm that is empirically shown to outperform the state-of-the-art methods in the real world; (3) A sequential process that allows informed disambiguation of possible hypotheses describing an agent’s plans. The long term impact of my contribution to computer science will be demonstrated by (1) developing new algorithms for plan recognition, intervention design and adaptation for exploratory settings; (2) showing that these methods generalize to different types of settings that differ widely in they type of interaction that is provided by the users.